def kde_plot_1d(ax, data, *args, **kwargs):
"""Plot a 1d marginalised distribution.
This functions as a wrapper around matplotlib.axes.Axes.plot, with a kernel
density estimation computation provided by scipy.stats.gaussian_kde in
between. All remaining keyword arguments are passed onwards.
Parameters
----------
ax: matplotlib.axes.Axes
axis object to plot on.
data: numpy.array
Samples to generate kernel density estimator.
weights: numpy.array, optional
Sample weights.
ncompress: int, optional
Degree of compression. Default 1000
xmin, xmax: float
lower/upper prior bound.
optional, default None
Returns
-------
lines: matplotlib.lines.Line2D
A list of line objects representing the plotted data (same as
matplotlib matplotlib.axes.Axes.plot command)
"""
if len(data) == 0:
return numpy.zeros(0), numpy.zeros(0)
if data.max() - data.min() <= 0:
return
xmin = kwargs.pop('xmin', None)
xmax = kwargs.pop('xmax', None)
weights = kwargs.pop('weights', None)
ncompress = kwargs.pop('ncompress', 1000)
x, w = sample_compression_1d(data, weights, ncompress)
kde = gaussian_kde(x, weights=w)
p = kde(x)
p /= p.max()
i = ((x < quantile(x, 0.999, w)) & (x > quantile(x, 0.001, w))) | (p > 0.1)
if xmin is not None:
i = i & (x > xmin)
if xmax is not None:
i = i & (x < xmax)
sigma = numpy.sqrt(kde.covariance[0, 0])
pp = cut_and_normalise_gaussian(x[i], p[i], sigma, xmin, xmax)
pp /= pp.max()
ans = ax.plot(x[i], pp, *args, **kwargs)
ax.set_xlim(*check_bounds(x[i], xmin, xmax), auto=True)
return ans
def fastkde_plot_1d(ax, data, *args, **kwargs):
"""Plot a 1d marginalised distribution.
This functions as a wrapper around matplotlib.axes.Axes.plot, with a kernel
density estimation computation provided by the package fastkde in between.
All remaining keyword arguments are passed onwards.
Parameters
----------
ax: matplotlib.axes.Axes
axis object to plot on
data: np.array
Uniformly weighted samples to generate kernel density estimator.
xmin, xmax: float
lower/upper prior bound
optional, default None
Returns
-------
lines: matplotlib.lines.Line2D
A list of line objects representing the plotted data (same as
matplotlib matplotlib.axes.Axes.plot command)
"""
if len(data) == 0:
return np.zeros(0), np.zeros(0)
if data.max() - data.min() <= 0:
return
xmin = kwargs.pop('xmin', None)
xmax = kwargs.pop('xmax', None)
cmap = kwargs.pop('cmap', None)
color = kwargs.pop('color', (next(ax._get_lines.prop_cycler)['color']
if cmap is None else cmap(0.68)))
q = kwargs.pop('q', '5sigma')
q = quantile_plot_interval(q=q)
try:
x, p = fastkde_1d(data, xmin, xmax)
except NameError:
raise ImportError("You need to install fastkde to use fastkde")
p /= p.max()
i = ((x > quantile(x, q[0], p)) & (x < quantile(x, q[1], p)))
ans = ax.plot(x[i], p[i], color=color, *args, **kwargs)
ax.set_xlim(*check_bounds(x[i], xmin, xmax), auto=True)
return ans
def hist_plot_1d(ax, data, *args, **kwargs):
"""Plot a 1d histogram.
This functions is a wrapper around matplotlib.axes.Axes.hist. All remaining
keyword arguments are passed onwards.
Parameters
----------
ax: matplotlib.axes.Axes
axis object to plot on
data: numpy.array
Samples to generate histogram from
weights: numpy.array, optional
Sample weights.
xmin, xmax: float
lower/upper prior bound.
optional, default data.min() and data.max()
cannot be None (reverts to default in that case)
Returns
-------
patches : list or list of lists
Silent list of individual patches used to create the histogram
or list of such list if multiple input datasets.
Other Parameters
----------------
**kwargs : `~matplotlib.axes.Axes.hist` properties
"""
if data.max() - data.min() <= 0:
return
xmin = kwargs.pop('xmin', None)
xmax = kwargs.pop('xmax', None)
plotter = kwargs.pop('plotter', '')
weights = kwargs.pop('weights', None)
if xmin is None:
xmin = quantile(data, 0.01, weights)
if xmax is None:
xmax = quantile(data, 0.99, weights)
histtype = kwargs.pop('histtype', 'bar')
if plotter == 'astropyhist':
try:
h, edges, bars = hist(data,
ax=ax,
range=(xmin, xmax),
histtype=histtype,
*args,
**kwargs)
except NameError:
raise ImportError("You need to install astropy to use astropyhist")
else:
h, edges, bars = ax.hist(data,
range=(xmin, xmax),
histtype=histtype,
weights=weights,
*args,
**kwargs)
if histtype == 'bar':
for b in bars:
b.set_height(b.get_height() / h.max())
elif histtype == 'step' or histtype == 'stepfilled':
trans = Affine2D().scale(sx=1, sy=1. / h.max()) + ax.transData
bars[0].set_transform(trans)
ax.set_xlim(*check_bounds(edges, xmin, xmax), auto=True)
ax.set_ylim(0, 1.1)
return bars
def hist_plot_2d(ax, data_x, data_y, *args, **kwargs):
"""Plot a 2d marginalised distribution as a histogram.
This functions as a wrapper around matplotlib.axes.Axes.hist2d
Parameters
----------
ax: matplotlib.axes.Axes
axis object to plot on
data_x, data_y: np.array
x and y coordinates of uniformly weighted samples to generate kernel
density estimator.
xmin, xmax, ymin, ymax: float
lower/upper prior bounds in x/y coordinates
optional, default None
levels: list
Shade iso-probability contours containing these levels of probability
mass. If None defaults to usual matplotlib.axes.Axes.hist2d colouring.
optional, default None
Returns
-------
c: matplotlib.collections.QuadMesh
A set of colors
"""
xmin = kwargs.pop('xmin', None)
xmax = kwargs.pop('xmax', None)
ymin = kwargs.pop('ymin', None)
ymax = kwargs.pop('ymax', None)
vmin = kwargs.pop('vmin', 0)
label = kwargs.pop('label', None)
levels = kwargs.pop('levels', None)
color = kwargs.pop('color', next(ax._get_lines.prop_cycler)['color'])
weights = kwargs.pop('weights', None)
if xmin is None or not np.isfinite(xmin):
xmin = quantile(data_x, 0.01, weights)
if xmax is None or not np.isfinite(xmax):
xmax = quantile(data_x, 0.99, weights)
if ymin is None or not np.isfinite(ymin):
ymin = quantile(data_y, 0.01, weights)
if ymax is None or not np.isfinite(ymax):
ymax = quantile(data_y, 0.99, weights)
rge = kwargs.pop('range', ((xmin, xmax), (ymin, ymax)))
if len(data_x) == 0 or len(data_y) == 0:
return np.zeros(0), np.zeros(0), np.zeros((0, 0))
cmap = kwargs.pop('cmap', basic_cmap(color))
if levels is None:
pdf, x, y, image = ax.hist2d(data_x, data_y, weights=weights,
cmap=cmap, range=rge, vmin=vmin,
*args, **kwargs)
else:
bins = kwargs.pop('bins', 10)
density = kwargs.pop('density', False)
cmin = kwargs.pop('cmin', None)
cmax = kwargs.pop('cmax', None)
pdf, x, y = np.histogram2d(data_x, data_y, bins, rge,
density, weights)
levels = iso_probability_contours(pdf, levels)
pdf = np.digitize(pdf, levels, right=True)
pdf = np.array(levels)[pdf]
pdf = np.ma.masked_array(pdf, pdf < levels[1])
if cmin is not None:
pdf[pdf < cmin] = np.ma.masked
if cmax is not None:
pdf[pdf > cmax] = np.ma.masked
image = ax.pcolormesh(x, y, pdf.T, cmap=cmap, vmin=vmin,
*args, **kwargs)
ax.patches += [plt.Rectangle((0, 0), 0, 0, fc=cmap(0.999), ec=cmap(0.32),
lw=2, label=label)]
ax.set_xlim(*check_bounds(x, xmin, xmax), auto=True)
ax.set_ylim(*check_bounds(y, ymin, ymax), auto=True)
return image
def kde_plot_1d(ax, data, *args, **kwargs):
"""Plot a 1d marginalised distribution.
This functions as a wrapper around matplotlib.axes.Axes.plot, with a kernel
density estimation computation provided by scipy.stats.gaussian_kde in
between. All remaining keyword arguments are passed onwards.
Parameters
----------
ax: matplotlib.axes.Axes
axis object to plot on.
data: np.array
Samples to generate kernel density estimator.
weights: np.array, optional
Sample weights.
ncompress: int, optional
Degree of compression. Default 1000
xmin, xmax: float
lower/upper prior bound.
optional, default None
levels: list
values at which to draw iso-probability lines.
optional, default [0.95, 0.68]
facecolor: bool or string
If set to True then the 1d plot will be shaded with the value of the
``color`` kwarg. Set to a string such as 'blue', 'k', 'r', 'C1' ect.
to define the color of the shading directly.
optional, default False
Returns
-------
lines: matplotlib.lines.Line2D
A list of line objects representing the plotted data (same as
matplotlib matplotlib.axes.Axes.plot command)
"""
if len(data) == 0:
return np.zeros(0), np.zeros(0)
if data.max()-data.min() <= 0:
return
kwargs = normalize_kwargs(
kwargs,
dict(linewidth=['lw'], linestyle=['ls'], color=['c'],
facecolor=['fc'], edgecolor=['ec']))
levels = kwargs.pop('levels', [0.95, 0.68])
xmin = kwargs.pop('xmin', None)
xmax = kwargs.pop('xmax', None)
weights = kwargs.pop('weights', None)
ncompress = kwargs.pop('ncompress', 1000)
density = kwargs.pop('density', False)
cmap = kwargs.pop('cmap', None)
color = kwargs.pop('color', (next(ax._get_lines.prop_cycler)['color']
if cmap is None else cmap(0.68)))
facecolor = kwargs.pop('facecolor', False)
if 'edgecolor' in kwargs:
edgecolor = kwargs.pop('edgecolor')
if edgecolor:
color = edgecolor
else:
edgecolor = color
q = kwargs.pop('q', '5sigma')
q = quantile_plot_interval(q=q)
if weights is not None:
data = data[weights != 0]
weights = weights[weights != 0]
x, w = sample_compression_1d(data, weights, ncompress)
kde = gaussian_kde(x, weights=w)
p = kde(x)
p /= p.max()
i = ((x > quantile(x, q[0], w)) & (x < quantile(x, q[1], w)))
if xmin is not None:
i = i & (x > xmin)
if xmax is not None:
i = i & (x < xmax)
sigma = np.sqrt(kde.covariance[0, 0])
pp = cut_and_normalise_gaussian(x[i], p[i], sigma, xmin, xmax)
pp /= pp.max()
area = np.trapz(x=x[i], y=pp) if density else 1
ans = ax.plot(x[i], pp/area, color=color, *args, **kwargs)
ax.set_xlim(*check_bounds(x[i], xmin, xmax), auto=True)
if facecolor and facecolor not in [None, 'None', 'none']:
if facecolor is True:
facecolor = color
c = iso_probability_contours_from_samples(pp, contours=levels,
weights=w)
cmap = basic_cmap(facecolor)
fill = []
for j in range(len(c)-1):
fill.append(ax.fill_between(x[i], pp, where=pp >= c[j],
color=cmap(c[j]), edgecolor=edgecolor))
return ans, fill
return ans
def fastkde_plot_1d(ax, data, *args, **kwargs):
"""Plot a 1d marginalised distribution.
This functions as a wrapper around matplotlib.axes.Axes.plot, with a kernel
density estimation computation provided by the package fastkde in between.
All remaining keyword arguments are passed onwards.
Parameters
----------
ax: matplotlib.axes.Axes
axis object to plot on
data: np.array
Uniformly weighted samples to generate kernel density estimator.
xmin, xmax: float
lower/upper prior bound
optional, default None
levels: list
values at which to draw iso-probability lines.
optional, default [0.95, 0.68]
facecolor: bool or string
If set to True then the 1d plot will be shaded with the value of the
``color`` kwarg. Set to a string such as 'blue', 'k', 'r', 'C1' ect.
to define the color of the shading directly.
optional, default False
Returns
-------
lines: matplotlib.lines.Line2D
A list of line objects representing the plotted data (same as
matplotlib matplotlib.axes.Axes.plot command)
"""
kwargs = normalize_kwargs(
kwargs,
dict(linewidth=['lw'], linestyle=['ls'], color=['c'],
facecolor=['fc'], edgecolor=['ec']))
if len(data) == 0:
return np.zeros(0), np.zeros(0)
if data.max()-data.min() <= 0:
return
levels = kwargs.pop('levels', [0.95, 0.68])
xmin = kwargs.pop('xmin', None)
xmax = kwargs.pop('xmax', None)
density = kwargs.pop('density', False)
cmap = kwargs.pop('cmap', None)
color = kwargs.pop('color', (next(ax._get_lines.prop_cycler)['color']
if cmap is None else cmap(0.68)))
facecolor = kwargs.pop('facecolor', False)
if 'edgecolor' in kwargs:
edgecolor = kwargs.pop('edgecolor')
if edgecolor:
color = edgecolor
else:
edgecolor = color
q = kwargs.pop('q', '5sigma')
q = quantile_plot_interval(q=q)
try:
x, p, xmin, xmax = fastkde_1d(data, xmin, xmax)
except NameError:
raise ImportError("You need to install fastkde to use fastkde")
p /= p.max()
i = ((x > quantile(x, q[0], p)) & (x < quantile(x, q[1], p)))
area = np.trapz(x=x[i], y=p[i]) if density else 1
ans = ax.plot(x[i], p[i]/area, color=color, *args, **kwargs)
ax.set_xlim(xmin, xmax, auto=True)
if facecolor and facecolor not in [None, 'None', 'none']:
if facecolor is True:
facecolor = color
c = iso_probability_contours(p[i], contours=levels)
cmap = basic_cmap(facecolor)
fill = []
for j in range(len(c)-1):
fill.append(ax.fill_between(x[i], p[i], where=p[i] >= c[j],
color=cmap(c[j]), edgecolor=edgecolor))
return ans, fill
return ans
def quantile(self, q=0.5, numeric_only=True, interpolation='linear'):
"""Weighted quantile of the sampled distribution."""
if not numeric_only:
raise NotImplementedError("numeric_only kwarg not implemented")
return quantile(self.values, q, self.weights, interpolation)
def quantile(self, q=0.5):
"""Weighted quantile of the sampled distribution."""
return quantile(self.values, q, self.weights)
def kde_plot_1d(ax, data, *args, **kwargs):
"""Plot a 1d marginalised distribution.
This functions as a wrapper around matplotlib.axes.Axes.plot, with a kernel
density estimation computation provided by scipy.stats.gaussian_kde in
between. All remaining keyword arguments are passed onwards.
Parameters
----------
ax: matplotlib.axes.Axes
axis object to plot on.
data: np.array
Samples to generate kernel density estimator.
weights: np.array, optional
Sample weights.
ncompress: int, optional
Degree of compression. Default 1000
xmin, xmax: float
lower/upper prior bound.
optional, default None
Returns
-------
lines: matplotlib.lines.Line2D
A list of line objects representing the plotted data (same as
matplotlib matplotlib.axes.Axes.plot command)
"""
if len(data) == 0:
return np.zeros(0), np.zeros(0)
if data.max() - data.min() <= 0:
return
kwargs = normalize_kwargs(kwargs,
dict(linewidth=['lw'],
linestyle=['ls'],
color=['c']),
drop=['fc', 'ec'])
xmin = kwargs.pop('xmin', None)
xmax = kwargs.pop('xmax', None)
weights = kwargs.pop('weights', None)
ncompress = kwargs.pop('ncompress', 1000)
cmap = kwargs.pop('cmap', None)
color = kwargs.pop('color', (next(ax._get_lines.prop_cycler)['color']
if cmap is None else cmap(0.68)))
q = kwargs.pop('q', '5sigma')
q = quantile_plot_interval(q=q)
if weights is not None:
data = data[weights != 0]
weights = weights[weights != 0]
x, w = sample_compression_1d(data, weights, ncompress)
kde = gaussian_kde(x, weights=w)
p = kde(x)
p /= p.max()
i = ((x > quantile(x, q[0], w)) & (x < quantile(x, q[1], w)))
if xmin is not None:
i = i & (x > xmin)
if xmax is not None:
i = i & (x < xmax)
sigma = np.sqrt(kde.covariance[0, 0])
pp = cut_and_normalise_gaussian(x[i], p[i], sigma, xmin, xmax)
pp /= pp.max()
ans = ax.plot(x[i], pp, color=color, *args, **kwargs)
ax.set_xlim(*check_bounds(x[i], xmin, xmax), auto=True)
return ans
